Cost-efficient high-resolution monitoring for specialty crops using AgI-GAN and AI-driven analytics

• AgI-GAN enhanced airplane imagery to 3.9 cm/pixel, matching UAV resolution. • Tree detection error reduced from 36.21% to 4.11% with AgI-GAN-enhanced images. • Data acquisition costs reduced by 60%, from $6.07 to $2.44 per acre. • AgI-GAN provides cost-effective, high-resolution data for specialty crop analysis. Unmanned aerial vehicles (UAVs) and airplane or satellite imagery are widely used technologies for data acquisition in precision agriculture, each with advantages and disadvantages. UAVs capture high-resolution images but have a limited collection range, leading to higher costs per acre. In contrast, airplane imagery is more cost-effective and covers larger areas but typically provides lower spatial resolution (15–50 cm), which is inadequate for assessing specialty crops. For example, Agroview, an AI-driven platform for plant inventories and plant-level analytics, requires high-resolution imagery (<10 cm/pixel) for optimal performance. This study aims to address this limitation by developing an agriculture intelligence generative adversarial network (AgI-GANs) to enhance airplane imagery (∼15 cm/pixel resolution) to resolutions comparable to UAV imagery (∼3.9 cm/pixel). The AgI-GAN-enhanced images significantly improved Agroview’s tree detection accuracy, reducing detection errors to below 10 %, with an average error of 4.11 % ± 4.5 across 10 citrus orchards, compared to 36.21 % ± 9 when using unenhanced airplane images. Additionally, cost analysis demonstrates that AgI-GAN-enhanced airplane imagery reduces data acquisition costs to approximately $2.44 per acre, a 60 % reduction from the $6.07 per acre cost associated with UAV data collection. This study highlights the transformative potential of AgI-GAN in precision agriculture, providing a cost-effective solution that combines the extensive coverage of airplane imagery with the high resolution required for precise data analytics, benefiting specialty crop growers and agricultural stakeholders.